
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class 					lowerCamelCase__				(							__lowercase):




	'''simple docstring'''




	_A								=	['image_processor', 'tokenizer']
	_A								=	'Pix2StructImageProcessor'
	_A								=	('T5Tokenizer', 'T5TokenizerFast')







	def __init__(   self						:Optional[int]	,  a						:List[Any]	,  a						:List[Any]		)						->					Dict:
								__UpperCamelCase      :  Optional[int]       =   False
								super().__init__(a	,  a		)







	def __call__(   self						:str	,  a						:Any=None	,  a						:Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None	,  a						:bool = True	,  a						:Union[bool, str, PaddingStrategy] = False	,  a						:Union[bool, str, TruncationStrategy] = None	,  a						:Optional[int] = None	,  a						:Optional[int] = 2_0_4_8	,  a						:int = 0	,  a						:Optional[int] = None	,  a						:Optional[bool] = None	,  a						:bool = False	,  a						:bool = False	,  a						:bool = False	,  a						:bool = False	,  a						:bool = False	,  a						:bool = True	,  a						:Optional[Union[str, TensorType]] = None	,  **a						:Union[str, Any]	,  )						->					BatchEncoding:
								if images is None and text is None:
															raise ValueError("You have to specify either images or text."		)

								# Get only text
								if images is None and not self.image_processor.is_vqa:
															__UpperCamelCase      :  Optional[Any]       =   self.tokenizer
															__UpperCamelCase      :  Dict       =   self.tokenizer(
															    text=a	,  add_special_tokens=a	,  padding=a	,  truncation=a	,  max_length=a	,  stride=a	,  pad_to_multiple_of=a	,  return_attention_mask=a	,  return_overflowing_tokens=a	,  return_special_tokens_mask=a	,  return_offsets_mapping=a	,  return_token_type_ids=a	,  return_length=a	,  verbose=a	,  return_tensors=a	,  **a	,  )
															return text_encoding

								if not self.image_processor.is_vqa:
															# add pixel_values
															__UpperCamelCase      :  Tuple       =   self.image_processor(
															    a	,  return_tensors=a	,  max_patches=a	,  **a		)
								else:
															# add pixel_values and bbox
															__UpperCamelCase      :  Tuple       =   self.image_processor(
															    a	,  return_tensors=a	,  max_patches=a	,  header_text=a	,  **a		)

								if text is not None and not self.image_processor.is_vqa:
															__UpperCamelCase      :  Dict       =   self.tokenizer(
															    text=a	,  add_special_tokens=a	,  padding=a	,  truncation=a	,  max_length=a	,  stride=a	,  pad_to_multiple_of=a	,  return_attention_mask=a	,  return_overflowing_tokens=a	,  return_special_tokens_mask=a	,  return_offsets_mapping=a	,  return_token_type_ids=a	,  return_length=a	,  verbose=a	,  return_tensors=a	,  **a	,  )

															if "attention_mask" in text_encoding:
																						__UpperCamelCase      :  Tuple       =   text_encoding.pop("attention_mask"		)
															if "input_ids" in text_encoding:
																						__UpperCamelCase      :  Tuple       =   text_encoding.pop("input_ids"		)
								else:
															__UpperCamelCase      :  str       =   None

								if text_encoding is not None:
															encoding_image_processor.update(a		)

								return encoding_image_processor







	def   _lowerCamelCase							(   self						:Union[str, Any]	,  *a						:str	,  **a						:List[str]		)						->					Dict:
								return self.tokenizer.batch_decode(*a	,  **a		)







	def   _lowerCamelCase							(   self						:Dict	,  *a						:Any	,  **a						:str		)						->					Dict:
								return self.tokenizer.decode(*a	,  **a		)







	@property
	def   _lowerCamelCase							(   self						:Dict		)						->					Any:
								__UpperCamelCase      :  str       =   self.tokenizer.model_input_names
								__UpperCamelCase      :  Any       =   self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)

from .testing import (
    are_the_same_tensors,
    execute_subprocess_async,
    require_bnb,
    require_cpu,
    require_cuda,
    require_huggingface_suite,
    require_mps,
    require_multi_gpu,
    require_multi_xpu,
    require_safetensors,
    require_single_gpu,
    require_single_xpu,
    require_torch_min_version,
    require_tpu,
    require_xpu,
    skip,
    slow,
)
from .training import RegressionDataset, RegressionModel, RegressionModelaXPU


from .scripts import test_script, test_sync, test_ops  # isort: skip